Diversity of lakes and ponds in the forest-tundra ecozone: from limnicity to limnodiversity
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Notice bibliographique
Résumé
Arctic and subarctic landscapes have unique hydrological and limnological features and are now experiencing rapid change due to climate warming and permafrost thaw. The highly abundant lakes, ponds, and rivers across these landscapes play an increasingly important role in global biogeochemical cycles and are sentinels of environmental changes. However, studying these remote waters poses challenges for both in situ sampling and remote-sensing analysis. Here we developed a synergistic remote-sensing strategy that combined PlanetScope and Sentinel-2 satellite data to estimate limnicity (water fraction per land surface), limnodensity (density of water bodies), and limnodiversity (optical diversity of water bodies) along a boreal forest-tundra transect, from the non-permafrost to the continuous permafrost zones of western Nunavik (Subarctic Canada). Our analyses show that this region hosts 335,281 water bodies, around 90% in the 0.0001 to 0.01 km2 size range. In bedrock outcrops, large water bodies were mostly associated with glacially carved depressions (higher limnicity). In contrast, small water bodies were predominately found in sedimentary infills along valleys (higher limnodensity). The discontinuous permafrost zone had the highest limnodensity and limnodiversity. This was likely due to permafrost thaw (thermokarst), particularly the collapse, subsidence, and erosion of palsas (organic permafrost mounds), resulting in ponds with black- and brown-colored waters, and lithalsas (mineral permafrost mounds), resulting in ponds with brown, light-brown, and sometimes white-colored waters. Some of these limnodense and limnodiverse landscapes, although covering only 2 to 7% of the total area of the study region, contained over one-third (34%) of the total number of water bodies, 97% of which were <0.01 km2; they accounted for a small proportion of the total black-colored water bodies (23%), but a high proportion of the total brown- (60%) and light brown-colored water bodies (92%) throughout the region. This research underscores the utility of optical satellite remote sensing for assessing water body types and for evaluating their individual and distinct aquatic responses to climate change. The dataset may be used to improve the modeling of carbon fluxes by better categorizing small water bodies affected by organic or mineral soil type settings. This is an important factor dictating biogeochemical responses, with effects on albedo, climate feedbacks, and ecosystem dynamics in the boreal forest-tundra region. The framework developed here may be applied to landscapes elsewhere in the world that have high densities of water bodies of variable size and optical properties.
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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle